Bulk RNAseq report - Differential Expression Analysis among conditions
Annamaria Piva
20 June, 2025
Last updated: 2025-06-20
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Knit directory: casper_ss_ma/analysis/
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Introduction
The goal of this analysis is to identify which pathways are up- or down-regulated in each condition (Healthy, HR+, and TNBC). The following comparisons have been performed:
HR+ vs Healthy
TNBC vs Healthy
TNBC vs HR+
Overview of the analysis step
The analysis includes:
- Differential gene expression analysis with DESeq2
- Gene set enrichment analysis with ClusterProfiler
- Gene set enrichment analysis with fastGSEA with the curated Human MSigDB Collections. In particular the hallmark gene sets summarize and represent specific well-defined biological states or processes.
- Focus on NF-kB pathway. The R package pathview allows to visualize differentially expressed genes in the KEGG pathway NF-kB pathway
The input for the following analysis is:
- counts matrix, produced by Salmon and normalized with variance stabilizing transformation (VST) normalization using DESeq2, where each row represents one sample and each column represents one gene, so each cell represents the expression level of a specific gene in a particular sample. VST aims at generating a matrix of values for which variance is constant across the range of mean values, especially for low mean;
- samples info, including sample name, condition (HRplus, TNBC, Healthy) and batch (240919_rnaseq, 250501_rnaseq).
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)Loading R packages and input data
The first steps to start the analysis in R is to load the packages required for the analysis, load the input data mentioned above and establish the thresholds for the analysis:
- min_sample = 2, minimum number of samples where the gene needs to have at least 1 read;
- logfc = log2(2) = 1, which represents the ratio between the expression level of a gene in the conditions considered, expressed in logarithmic scale (base 2); a positive log fold change for a gene, greater than 1, means that the expression of that specific gene is increased in group1 with respect to group2, by a multiplicative factor 2^logfc;
- qvalue = 0.01, that can be interpreted as false positive rate, the proportion of false positives among all positive results, which means avoid to detect differential expression of a gene that is not differentially expressed. LogFC and qvalue thresholds have been selected based on commonly used thresholds.
Quality control
PCA
Let’s have a look at PCA, and gene expression pattern across samples. The batch effect has been considered in the design.
Correlation and distance between samples
To evaluate the similarity between RNA-seq samples, we computed both Pearson correlation and Euclidean distance using variance-stabilized expression data:
Pearson correlation (cor()): Measures the degree to which gene expression profiles across samples vary in a similar pattern. Values range from –1 (inverse relationship) to +1 (perfect similarity). High correlations indicate that samples share similar expression trends.
Euclidean distance (dist()): Quantifies the overall dissimilarity in expression profiles between samples, based on their absolute expression values. Smaller distances indicate more similar samples.
These metrics provide complementary views: correlation focuses on shared expression patterns (direction), while distance captures overall differences in magnitude. Both are useful for identifying sample clusters, detecting outliers, and validating experimental reproducibility.
Differential expression analysis
Differential expression analysis is performed using a custom function, which accounts for batch effect. A batch effect occurs when non-biological factors, like laboratory conditions or instruments used, in an experiment cause changes in the data produced by the experiment. Lowly expressed genes are removed to reduce noise. Lowly expressed genes are here considered as:
- genes having total number of reads less than half of the samples;
- genes expressed in less samples than the number of conditions.
Contrast 1: HRplus vs HLT
MA plot and volcano plot
Genes are annotated as significant or not, to distinguish between genes showing meaningful changes, that is having an adjusted p-value below the threshold considered above and an absolute log2FoldChange greater than the cutoff considered above.
Table of all differentially expressed genes
Heatmap for top 20 genes
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Gene set enrichment analysis
Further analysis is done through gene set enrichment analysis, which does not exclude genes based on logfc or adjusted p-value, as done previously. GSEA is performed separately on each subontology: biological processes (BP), cellular components (CC) and molecular functions (MF). The dot plot below shows the top 10 most enriched GO terms. The size of each dot correlates with the count of differentially expressed genes associated with each GO term. Furthermore, the color of each dot reflects the significance of the enrichment of the respective GO term, highlighting its relative importance.
GSEA msigdbr
quartz_off_screen
2 Pathway viewer: focus on NF-kB signaling pathway (KEGG id: hsa04064)
To visualize gene expression changes on biological pathways, we used the pathview R package, which maps gene-level statistics (e.g., log2 fold-changes) onto KEGG pathway diagrams.
For each contrast in our differential expression analysis, we extracted significantly differentially expressed genes and passed their log2 fold-change values to pathview() to visualize the NF-kappa B signaling pathway (KEGG pathway ID “hsa04064”). Pathway visualizations highlight upregulated and downregulated genes in red and blue, respectively, based on log2 fold-change.
Contrast 2: TNBC_vs_HLT
MA plot and volcano plot
Genes are annotated as significant or not, to distinguish between genes showing meaningful changes, that is having an adjusted p-value below the threshold considered above and an absolute log2FoldChange greater than the cutoff considered above.
Heatmap for top 20 genes
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Gene set enrichment analysis
GSEA msigdb
quartz_off_screen
2 Contrast 3: TNBC vs HRplus
MA plot and volcano plot
Genes are annotated as significant or not, to distinguish between genes showing meaningful changes, that is having an adjusted p-value below the threshold considered above and an absolute log2FoldChange greater than the cutoff considered above.
Heatmap for top 20 genes
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Gene set enrichment analysis
GSEA msigdb
quartz_off_screen
2 Common pathways
Biological Processes Pathways
Cellular Components Pathways
Molecular Functions Pathways
GSEA Heatmap Hallmarks - all comparisons
